Course details

Modern Methods of Speech Processing

MZD Acad. year 2020/2021 Winter semester

Current academic year

From simple systems to stochastic modelling. Hidden Markov models. Large vocabulary continuous speech recognition. Language models. Speech production, speech perception: time and frequency. Data-driven methods for feature extraction. Speech databases. Excitation in speech coding, CELP. Speaker identification.

Guarantor

Černocký Jan, prof. Dr. Ing. (DCGM)

Language of instruction

Czech, English

Completion

Examination (written)

Time span

  • 39 hrs lectures

Assessment points

  • 100 pts final exam

Department

Department of Computer Graphics and Multimedia (UPGM)

Lecturer

Černocký Jan, prof. Dr. Ing. (DCGM)

Instructor

Černocký Jan, prof. Dr. Ing. (DCGM)

Subject specific learning outcomes and competences

This course allows students to implement simple speech processinga pplications, as for example voice command of a process. However, first of all it enables them to join the development of complex systems for speech recognition and coding systems, using modern methods, in academic and industrial environments.

Learning objectives

We will mention methods currently implemented in industrial applications (such as mobile phones or commercially available recognizers) but will not promissing methods existing so far only in laboratories. Attention will be paid to techniques derived using data and inspired by human autition and speech production.

Prerequisite knowledge and skills

basic knowledge of digitial signal processing, having attended a basic course on speech processing is advantageous.

Study literature

  • Moore, B.C.J., : An introduction to the psychology of hearing, Academic Press, 1989
  • Jelinek, F.: Statistical Methods for Speech Recognition, MIT Press, 1998
  • Fukunaga, K.: Introduction to Statistical Pattern Recognition, Academic Press, 1990
  • Vapnik, V. N.: Statistical Learning Theory, Wiley-Interscience, 1998
  • Dutoit, T.: An Introduction to Text-To-Speech Synthesis, Kluwer Academic Publishers, 1997
  • Ben Gold, Nelson Morgan, Dan Ellis: Speech and Audio Signal Processing: Processing and Perception of Speech and Music Hardcover, Wiley-Interscience; 2nd Edition, 2011.
  • Psutka, J.: Komunikace s s počítačem mluvenou řečí. Academia, Praha, 1995
  • Dong Yu, Li Deng:  Automatic Speech Recognition: A Deep Learning Approach, Springer, 2014.
  • Gold, B., Morgan, N.: Speech and audio signal processing, John Wiley & Sons, 2000
  • Homayoon Beigi: Fundamentals of Speaker Recognition, Springer, 2011
  • Daniel Jurafsky, James H. Martin: SPEECH & LANGUAGE PROCESSING, 2nd edition,  Prentice Hall, 2008.
  • Texts from http://www.fit.vutbr.cz/~cernocky/speech/

Syllabus of lectures

  1. Review of notions: signal vectors and parameter matrices, basic statistics.
  2. Stochastic modeling of parameters, modeling of time by state sequences.
  3. Hidden Markov models: basic structure, training.
  4. Recognition of speech using HMM: Viterbi search, token passing.
  5. Pronunciation dictionaries and language models.
  6. Speech production and derived parameters: LPC, Log area ratios, line spectral pairs.
  7. Speech perception and derived parameters: Mel-frequency cepstral coefficients, Perceptual linear prediction.
  8. Temporal properties of hearing - RASTA filtering.
  9. Training the feature extractor on the data - linear discriminant analysis.
  10. Speech databases: standards, contents, speakers, annotations.
  11. Vocoders and modeling of the excitation: multi-pulse and stochastic excitations (GSM coding).
  12. CELP coding: long-term predictor, codebooks. Very low bit-rate coders.
  13. Current methods of speaker identification and verification.

Controlled instruction

attending the course is not checked, the evaluation of the course is upon the results of exam or final report.

Course inclusion in study plans

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